Liu Yong, Yang Hong, Gong Shan Shan, Liu Ya Qing, Xiong Xing Zhong
Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China.
Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Zigong 643000, China.
Math Biosci Eng. 2020 Jul 31;17(5):5173-5189. doi: 10.3934/mbe.2020280.
Activity recognition benefits the lives of residents in a smart home on a daily basis. One of the aims of this technology is to achieve good performance in activity recognition. The extraction and selection of the daily activity feature have a significant effect on this performance. However, commonly used extraction of daily activity features have limited the performance of daily activity recognition. Based on the nature of the time series of sensor events caused by daily activities, this paper presents a novel extraction approach for daily activity feature. First, time tuples are extracted from sensor events to form a time series. Subsequently, several common statistic formulas are proposed to form the space of daily activity features. Finally, a feature selection algorithm is employed to generate final daily activity features. To evaluate the proposed approach, two distinct datasets are adopted for activity recognition based on four different classifiers. The results of the experiment reveal that the proposed approach is an improvement over the commonly used approach.
活动识别在日常生活中使智能家居中的居民受益。这项技术的目标之一是在活动识别方面取得良好性能。日常活动特征的提取和选择对该性能有重大影响。然而，常用的日常活动特征提取方法限制了日常活动识别的性能。基于日常活动引起的传感器事件时间序列的性质，本文提出了一种新颖的日常活动特征提取方法。首先，从传感器事件中提取时间元组以形成时间序列。随后，提出了几个常用统计公式以形成日常活动特征空间。最后，采用一种特征选择算法来生成最终的日常活动特征。为了评估所提出的方法，基于四个不同的分类器采用两个不同的数据集进行活动识别。实验结果表明，所提出的方法比常用方法有所改进。
